%reload_ext pretty_jupyter

import os
import zipfile
import random

import numpy as np
import pandas as pd

import plotly_express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import plotly.offline as pyo
import plotly.io as pio

import kaggle
from tqdm import tqdm

import cv2

import tensorflow as tf
import warnings
warnings.filterwarnings('ignore')
pyo.init_notebook_mode()

df = pd.read_csv("data/1990-01-01_mag-6.csv")
df = df.sort_values(by='time', ascending=True)

# -.-|m {output: false}

df['date'] = pd.to_datetime(df['time']).dt.normalize()
df['month'] = pd.to_datetime(df['time']).dt.to_period('M')
df['year'] = pd.to_datetime(df['time']).dt.to_period('Y')
df.index = np.arange(1, len(df) + 1)

df1 = df.groupby('year')['mag'].max()
fig = px.bar(df1, x=df1.index.to_timestamp(), y='mag')
fig.show()

df1 = df.groupby('year')['mag'].mean()
fig = px.bar(df1, x=df1.index.to_timestamp(), y='mag')
fig.show()

px.set_mapbox_access_token("pk.eyJ1IjoiZnZhZmhhZSIsImEiOiJja3E5ZGhxaGowOGQxMm9ueXFqYXpzNG5mIn0.-u7PlWQhs-Tnpq87GLptcA")
fig = px.scatter_geo(df, lat="latitude", lon="longitude", color="mag",
                     animation_frame="year",
                     range_color=(min(df.mag), max(df.mag)), height=700)
fig.show()

px.set_mapbox_access_token("pk.eyJ1IjoiZnZhZmhhZSIsImEiOiJja3E5ZGhxaGowOGQxMm9ueXFqYXpzNG5mIn0.-u7PlWQhs-Tnpq87GLptcA")
fig = px.scatter_geo(df, lat="latitude", lon="longitude", color="mag",
                     animation_frame="month",
                     range_color=(min(df.mag), max(df.mag)), height=700)
fig.show()

df_globe = df.sort_values(by="mag", ascending=False).copy()[:100]

fig = go.Figure(data=go.Scattergeo( 
    lon = df_globe['longitude'], 
    lat = df_globe['latitude'], 
    text = [f'Magnitude: {mag} <br> Date: {date}' for mag, date in zip(df_globe['mag'], pd.to_datetime(df_globe['time']).dt.to_period("D"))], 
    mode = 'markers', 
    marker = dict( 
        size = 8, 
        opacity = 0.8, 
        autocolorscale = False, 
        colorscale = 'Reds', 
        symbol = 'diamond', 
        cmin = df_globe['mag'].min(), 
        color = df_globe['mag'], 
        cmax = df_globe['mag'].max(), 
        colorbar_title="Earthquake Magnitude <br> 7 Richter and up" ) 
    )
)

fig.update_geos( projection_type="orthographic", landcolor="green", oceancolor="MidnightBlue", showocean=True, lakecolor="LightBlue" )

fig.update_layout( title = '100 most powerful earthquakes since the year 1990', xaxis_rangeslider_visible=True, height = 700 )

pio.show(fig)

keep = ['latitude', 'longitude', 'depth', 'mag', 'place']

df_globe = df.sort_values(by="mag", ascending=False).copy()[:100].drop(columns=[x for x in df_globe.columns if x not in keep])
df_globe.index = np.arange(1, len(df_globe) + 1)